AI-Driven Intrusion Detection and Energy-Efficient Routing in Internet of Things (IoT) Networks

MAMOUN YAQOUB MMOUN; Abdulghader Jalgum

AI-Driven Intrusion Detection and Energy-Efficient Routing in Internet of Things (IoT) Networks

Author(s)	Mr. MAMOUN YAQOUB MMOUN, Mr. Abdulghader Jalgum
Country	India
Abstract	The Internet of Things (IoT) has become an important part of modern communication systems by connecting sensors, smart devices, wearable technologies, industrial equipment, and other physical objects through the internet. Although IoT networks provide many benefits, they also face serious challenges related to cybersecurity and energy consumption. Most IoT devices have limited processing power, memory, and battery capacity, making them vulnerable to cyberattacks and difficult to protect using traditional security methods. This paper proposes an AI-driven framework for intrusion detection and energy-efficient routing in IoT networks. The framework uses machine-learning techniques to identify malicious network traffic and classify it as normal or abnormal. At the same time, it applies an energy-aware routing strategy to select communication paths based on node energy level, link quality, delay, and security status. The proposed approach aims to improve attack detection while reducing unnecessary energy consumption in IoT devices. The framework can be evaluated using security metrics such as accuracy, precision, recall, F1-score, and false-positive rate, as well as network metrics including packet delivery ratio, delay, throughput, residual energy, and network lifetime. The study is expected to show that combining artificial intelligence with energy-aware routing can improve both the security and efficiency of IoT networks.
Field	Computer > Network / Security
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-12

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International Journal For Multidisciplinary Research

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AI-Driven Intrusion Detection and Energy-Efficient Routing in Internet of Things (IoT) Networks

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